What Doubling Tricks Can and Can't Do for Multi-Armed Bandits

19 March 2018

Papers citing "What Doubling Tricks Can and Can't Do for Multi-Armed Bandits"

30 / 30 papers shown

Title
Robust Online Learning with Private Information Kyohei Okumura 176 0 0 08 May 2025
Multi-agent Multi-armed Bandits with Minimum Reward Guarantee Fairness Piyushi Manupriya Himanshu S. Jagarlapudi Ganesh Ghalme FaML 118 0 0 21 Feb 2025
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler Yaxiong Liu Atsuyoshi Nakamura Kohei Hatano Eiji Takimoto 15 0 0 28 Jun 2023
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback Fares Fourati Vaneet Aggarwal Christopher J. Quinn Mohamed-Slim Alouini 57 14 0 02 Feb 2023
Local Differential Privacy for Sequential Decision Making in a Changing Environment Pratik Gajane 51 1 0 02 Jan 2023
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design Rui Ai Boxiang Lyu Zhaoran Wang Zhuoran Yang Michael I. Jordan 72 3 0 19 Oct 2022
Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Model Chengju Chen Canzhe Zhao Shuai Li 29 5 0 12 Jul 2022
Autonomous Drug Design with Multi-Armed Bandits Hampus Gummesson Svensson E. Bjerrum C. Tyrchan Ola Engkvist M. Chehreghani 90 5 0 04 Jul 2022
Provably Efficient Model-Free Constrained RL with Linear Function Approximation A. Ghosh Xingyu Zhou Ness B. Shroff 146 28 0 23 Jun 2022
Bayesian Optimization under Stochastic Delayed Feedback Arun Verma Zhongxiang Dai Bryan Kian Hsiang Low 80 12 0 19 Jun 2022
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds Shinji Ito Taira Tsuchiya Junya Honda AAML 43 17 0 14 Jun 2022
Logarithmic regret bounds for continuous-time average-reward Markov decision processes Xuefeng Gao X. Zhou 116 8 0 23 May 2022
The Primacy Bias in Deep Reinforcement Learning Evgenii Nikishin Max Schwarzer P. DÓro Pierre-Luc Bacon Rameswar Panda OnRL 150 196 0 16 May 2022
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits Jiatai Huang Yan Dai Longbo Huang 62 19 0 28 Jan 2022
Bridging Adversarial and Nonstationary Multi-armed Bandit Ningyuan Chen Shuoguang Yang Hailun Zhang AAML 73 4 0 05 Jan 2022
Bandit problems with fidelity rewards Gábor Lugosi Ciara Pike-Burke Pierre-André Savalle 47 0 0 25 Nov 2021
Dynamic Selection in Algorithmic Decision-making Jin Li Ye Luo Xiaowei Zhang 89 2 0 28 Aug 2021
From Finite to Countable-Armed Bandits Anand Kalvit A. Zeevi 82 14 0 22 May 2021
Learning to Persuade on the Fly: Robustness Against Ignorance You Zu Krishnamurthy Iyer Haifeng Xu 146 34 0 19 Feb 2021
Nonstochastic Bandits with Infinitely Many Experts X. Meng Tuhin Sarkar M. Dahleh OffRL 54 1 0 09 Feb 2021
Generalized non-stationary bandits Anne Gael Manegueu Alexandra Carpentier Yi Yu 82 10 0 01 Feb 2021
Sequential Choice Bandits with Feedback for Personalizing users' experience A. Rangi M. Franceschetti Long Tran-Thanh 23 2 0 05 Jan 2021
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation Jiafan He Dongruo Zhou Quanquan Gu 60 95 0 23 Nov 2020
Sample Efficient Reinforcement Learning with REINFORCE Junzi Zhang Jongho Kim Brendan O'Donoghue Stephen P. Boyd 115 113 0 22 Oct 2020
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping Dongruo Zhou Jiafan He Quanquan Gu 91 136 0 23 Jun 2020
Social Learning in Multi Agent Multi Armed Bandits Abishek Sankararaman A. Ganesh Sanjay Shakkottai 109 86 0 04 Oct 2019
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound Lin F. Yang Mengdi Wang OffRL GP 116 288 0 24 May 2019
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players Etienne Boursier E. Kaufmann Abbas Mehrabian Vianney Perchet 78 63 0 04 Feb 2019
Individual Fairness in Hindsight Swati Gupta Vijay Kamble FaML 79 63 0 10 Dec 2018
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits Julian Zimmert Yevgeny Seldin AAML 199 182 0 19 Jul 2018